An x-ray diagnostics device of this kind, known from German patent DE 100 37 735 A1, is shown as an example in FIG. 1. Said device has a support 1 supporting a rotatable C-arm 2 which has an x-ray emitter 3 and an x-ray image detector 4 attached to its ends.
As an alternative to the stand 1 shown, floor and/or ceiling mounted stands can also be used. The C-arm 2 can also be replaced by a C-arm 2 of the type known as an electronic C-arm 2, in which the x-ray emitter 3 and the x-ray image detector 4 are electronically coupled.
The x-ray image detector 4 can be a flat semiconductor detector, rectangular or square in form, and preferably made from amorphous silicon (aSi).
A patient support table 5 for recording a heart of a patient undergoing investigation for example is located in the beam path of the x-ray emitter 3. Connected to the x-ray diagnostics device is an image system 6 which receives and processes the image signals of the x-ray image detector 4.
High-resolution images in x-ray diagnostics are fundamental for a reliable and accurate diagnosis. The aim in this respect is to make even the smallest details visible in high quality resolution. The principal means of influencing image quality in x-ray diagnostics is via the administered x-ray dose. The x-ray dose, however, mainly influences the image noise in an x-ray image. In very general terms a high x-ray dose produces a noise-free image.
The use of flat image detectors (FD) generally has no direct influence on the resolution of an x-ray image. This aspect depends to a significant extent on the pixel resolution of the detector system.
So-called zoom formats on C-arm systems form the prior art for displaying a high resolution x-ray image. Instead of using the whole x-ray image detector to generate images, these methods use only a small part of the surface so that the image appears enlarged. However, this method eventually reaches its limit at the available resolution of the x-ray image amplifier (IA) or flat image detector (FD), in that it is unable to display anatomical details which are smaller than the resolution of which the x-ray image detector is physically capable. Even image interpolation methods, which use procedures such as bicubic interpolation to compute individual images up to a higher resolution, cannot bring out details which are too small to be seen.
The only solution for improving the resolution capability of IA and FD systems is to change the x-ray image detectors at considerable expense. This means that an improved x-ray image detector must provide for example 2048×2048 pixels instead of 1024×1024 pixels over the same area. However, this places great demands on the detector manufacturers, who have now reached the limits of what is technically possible at the present time, not to mention the costs that a new x-ray image detector entails. Furthermore the area of each individual pixel, which decreases with increasing resolution, has a direct influence on the x-ray quantum yield, and thus also on such matters as the noise content of the x-ray image for instance.
In summary, the technical options for increasing the pixel resolution are very limited.
For this reason, in an earlier patent application DE 10 2005 010 119.4 it was proposed that the source-image distance (SID) for present-day C-arm systems be changed so that an image sequence containing low resolution images is created using a different distance (SID), the coordinate systems are then aligned and a high resolution image known as a C-arm super-resolution image is computed from the said images. To generate super-resolution images, however, this solution requires this special recording process. But the x-ray systems used for diagnostic purposes are not generally C-arm systems, since the latter are too dear and have too many features to be used for making a normal x-ray image. The C-arm solution mentioned above—varying the SID—cannot be used in present-day simple systems, since such systems do not generally make provision for the SID to be varied.
A similar problem also occurs in other areas where images are recorded using common video and photographic cameras, for example. Technically the resolution of photographic cameras cannot be increased at will. In applications requiring a higher degree of detail in the images, such as satellite imaging and military surveillance, methods using a plurality of separate recorded images from which to compute a single high resolution image have been known for a considerable time under the generic term “super-resolution”, as described for instance in “Advances and Challenges in Super-Resolution” by Sina Farsiu et al., Invited Paper, International Journal of Imaging Systems and Technology, Special Issue on High Resolution Image Reconstruction, Vol. 14, No. 2, pages 47 to 57, 2004.
In the medical field, the use of a super-resolution approach to the generation of high-resolution MRI images is described only in “Super-Resolution in MRI: Application to Human White Matter Fiber Tract Visualization by Diffusion Tensor Imaging” by Sharon Peled et al., Magnetic Resonance in Medicine, 45, pages 29 to 35 (2001).
The functional principle of super-resolution approaches is based on the premise that input is available in the form of an image sequence consisting of a plurality of images which can be registered against one another by a suitable, usually affine but also flexible transformation, that is, images having a suitable type of “movement”. In the case of satellite imaging or video sequences recorded using a video camera, said suitable transformation may be achieved by a scene shift in the image. This translation fulfills the requirement for an affine transformation and is very easy to produce.
According to M. Elad et al., “Super-Resolution Reconstruction of Image Sequences” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 9, pages 817 to 834, September 1999, the general model for super-resolution can be described as follows: Low resolution images gi in an image sequence result from the projection P of a high-resolution image f onto their image plane and the adjustment of their coordinate systems by an affine 2D transformation. Only the low resolution images can be observed—the high resolution image cannot be observed due to the limited capabilities of the camera. It follows from this that the images gi are all offset relative to one another and in fact must be offset for the approach to work.
The super-resolution principle will now be explained with the aid of FIG. 2. Each box, whether large or small, represents a single pixel. FIG. 2 shows a first low resolution image 7 having pixels 10 and a second image 8 shifted in the x and y directions and having the same low resolution, both images being intended for inclusion in a high resolution image 9 having pixels 11 my means of a transformation. The area of the pixels 11 in the high-resolution image 9 is small, and the area of the pixels 10 in the original low resolution images 7 and 8 is large in comparison.
The coordinate system offset necessary for super-resolution is very easy to create for satellite imaging and video recording:                With satellite imaging:                    The satellite travels around the earth all by itself. The recorded images are therefore offset from one another.                        With video recording:                    A suitable movement can be introduced very simply on a manual basis.                        
In both cases therefore a shifted scene of low resolution images forms the raw material for a high-resolution image.